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Course Outline
Introduction to ROS and Python for Robotics
- Overview of ROS features and architecture
- Benefits of using ROS for mobile robotics
Understanding ROS
- Core concepts and components
- ROS file system, directory structure, and communication model
Setting up the Development Environment
- Installation of ROS and Python
- Configuration of ROS environment and workspace
- Connecting a mobile robot platform with ROS
Creating and Running ROS Nodes with Python
- Creating ROS nodes using Python
- Running nodes and using command line tools
- Writing and using ROS node launch files
- Utilizing ROS parameters and logging
Creating and Using ROS Topics with Python
- Creating ROS topics with Python
- Publishing and subscribing to ROS topics
- Utilizing ROS message types and custom messages
- Monitoring and recording ROS topics using ROS tools
Creating and Using ROS Services with Python
- Creating ROS services with Python
- Requesting and providing ROS services
- Utilizing ROS service types and custom services
- Inspecting and calling ROS services using ROS tools
Creating and Using ROS Actions with Python
- Creating ROS actions with Python
- Sending and receiving ROS action goals
- Utilizing ROS action types and custom actions
- Managing and canceling ROS actions using ROS tools
Using ROS Packages and Libraries for Mobile Robots
- Using ROS navigation stack for mobile robots
- Implementing ROS SLAM packages for mobile robots
- Employing ROS perception packages for mobile robots
Integrating ROS with Other Frameworks and Tools
- Using ROS with OpenCV for computer vision
- Using ROS with TensorFlow for machine learning
- Using ROS with Gazebo for simulation
- Using ROS with other frameworks and tools
Troubleshooting and Debugging ROS Applications
- Addressing common issues and errors in ROS applications
- Applying effective debugging techniques and tools
- Tips and best practices for improving ROS performance
Summary and Next Steps
Requirements
- An understanding of basic robotics concepts and terminology
- Experience with Python programming and data analysis
- Familiarity with Linux operating system and command line tools
Audience
- Robotics developers
- Robotics enthusiasts
21 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
Contact us for an exact quote and to hear our latest promotions
Public Training
Please see our public courses
Testimonials (5)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
I mostly enjoyed everything.
